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Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity
Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning to inform the design of a semi-continuous algal cul...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795378/ https://www.ncbi.nlm.nih.gov/pubmed/35087023 http://dx.doi.org/10.1038/s41467-021-27665-y |
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author | Long, Bin Fischer, Bart Zeng, Yining Amerigian, Zoe Li, Qiang Bryant, Henry Li, Man Dai, Susie Y. Yuan, Joshua S. |
author_facet | Long, Bin Fischer, Bart Zeng, Yining Amerigian, Zoe Li, Qiang Bryant, Henry Li, Man Dai, Susie Y. Yuan, Joshua S. |
author_sort | Long, Bin |
collection | PubMed |
description | Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning to inform the design of a semi-continuous algal cultivation (SAC) to sustain optimal cell growth and minimize mutual shading. An aggregation-based sedimentation (ABS) strategy is then designed to achieve low-cost biomass harvesting and economical SAC. The ABS is achieved by engineering a fast-growing strain, Synechococcus elongatus UTEX 2973, to produce limonene, which increases cyanobacterial cell surface hydrophobicity and enables efficient cell aggregation and sedimentation. SAC unleashes cyanobacterial growth potential with 0.1 g/L/hour biomass productivity and 0.2 mg/L/hour limonene productivity over a sustained period in photobioreactors. Scaling-up the SAC with an outdoor pond system achieves a biomass yield of 43.3 g/m(2)/day, bringing the minimum biomass selling price down to approximately $281 per ton. |
format | Online Article Text |
id | pubmed-8795378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87953782022-02-07 Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity Long, Bin Fischer, Bart Zeng, Yining Amerigian, Zoe Li, Qiang Bryant, Henry Li, Man Dai, Susie Y. Yuan, Joshua S. Nat Commun Article Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning to inform the design of a semi-continuous algal cultivation (SAC) to sustain optimal cell growth and minimize mutual shading. An aggregation-based sedimentation (ABS) strategy is then designed to achieve low-cost biomass harvesting and economical SAC. The ABS is achieved by engineering a fast-growing strain, Synechococcus elongatus UTEX 2973, to produce limonene, which increases cyanobacterial cell surface hydrophobicity and enables efficient cell aggregation and sedimentation. SAC unleashes cyanobacterial growth potential with 0.1 g/L/hour biomass productivity and 0.2 mg/L/hour limonene productivity over a sustained period in photobioreactors. Scaling-up the SAC with an outdoor pond system achieves a biomass yield of 43.3 g/m(2)/day, bringing the minimum biomass selling price down to approximately $281 per ton. Nature Publishing Group UK 2022-01-27 /pmc/articles/PMC8795378/ /pubmed/35087023 http://dx.doi.org/10.1038/s41467-021-27665-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Long, Bin Fischer, Bart Zeng, Yining Amerigian, Zoe Li, Qiang Bryant, Henry Li, Man Dai, Susie Y. Yuan, Joshua S. Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title | Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title_full | Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title_fullStr | Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title_full_unstemmed | Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title_short | Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title_sort | machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795378/ https://www.ncbi.nlm.nih.gov/pubmed/35087023 http://dx.doi.org/10.1038/s41467-021-27665-y |
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